onnxruntime/BUILD.md

Build ONNX Runtime

Supported dev environments

OS	Supports CPU	Supports GPU	Notes
Windows 10	YES	YES	Must use VS 2017 or the latest VS2015
Windows 10 Subsystem for Linux	YES	NO
Ubuntu 16.x	YES	YES
Ubuntu 17.x	YES	YES
Ubuntu 18.x	YES	YES
Fedora 24	YES	YES
Fedora 25	YES	YES
Fedora 26	YES	YES
Fedora 27	YES	YES
Fedora 28	YES	NO	Cannot build GPU kernels but can run them

• Red Hat Enterprise Linux and CentOS are not supported.
• GCC 4.x and below are not supported. If you are using GCC 7.0+, you'll need to upgrade eigen to a newer version before compiling ONNX Runtime.

OS/Compiler Matrix:

OS/Compiler	Supports VC	Supports GCC	Supports Clang
Windows 10	YES	Not tested	Not tested
Linux	NO	YES(gcc>=5.0)	YES

ONNX Runtime python binding only supports Python 3.x. Please use python 3.5+.

Build

Install cmake-3.11 or better from https://cmake.org/download/.

Checkout the source tree:

git clone --recursive https://github.com/Microsoft/onnxruntime
cd onnxruntime
./build.sh for Linux (or ./build.bat for Windows)

The build script runs all unit tests by default.

The complete list of build options can be found by running ./build.sh (or ./build.bat) --help

Build/Test Flavors for CI

CI Build Environments

Build Job Name	Environment	Dependency	Test Coverage	Scripts
Linux_CI_Dev	Ubuntu 16.04	python=3.5	Unit tests; ONNXModelZoo	script
Linux_CI_GPU_Dev	Ubuntu 16.04	python=3.5; nvidia-docker	Unit tests; ONNXModelZoo	script
Windows_CI_Dev	Windows Server 2016	python=3.5	Unit tests; ONNXModelZoo	script
Windows_CI_GPU_Dev	Windows Server 2016	cuda=9.0; cudnn=7.0; python=3.5	Unit tests; ONNXModelZoo	script

Additional Build Flavors

The complete list of build flavors can be seen by running ./build.sh --help or ./build.bat --help. Here are some common flavors.

Windows CUDA Build

ONNX Runtime supports CUDA builds. You will need to download and install CUDA and CUDNN.

ONNX Runtime is built and tested with CUDA 9.0 and CUDNN 7.0 using the Visual Studio 2017 14.11 toolset (i.e. Visual Studio 2017 v15.3). CUDA versions up to 9.2 and CUDNN version 7.1 should also work with versions of Visual Studio 2017 up to and including v15.7, however you may need to explicitly install and use the 14.11 toolset due to CUDA and CUDNN only being compatible with earlier versions of Visual Studio 2017.

To install the Visual Studio 2017 14.11 toolset, see https://blogs.msdn.microsoft.com/vcblog/2017/11/15/side-by-side-minor-version-msvc-toolsets-in-visual-studio-2017/

If using this toolset with a later version of Visual Studio 2017 you have two options:

1. Setup the Visual Studio environment variables to point to the 14.11 toolset by running vcvarsall.bat prior to running cmake

   • e.g. if you have VS2017 Enterprise, an x64 build would use the following command "C:\Program Files (x86)\Microsoft Visual Studio\2017\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" amd64 -vcvars_ver=14.11

2. Alternatively if you have CMake 3.12 or later you can specify the toolset version in the "-T" parameter by adding "version=14.11"

   • e.g. use the following with the below cmake command -T version=14.11,host=x64

CMake should automatically find the CUDA installation. If it does not, or finds a different version to the one you wish to use, specify your root CUDA installation directory via the -DCUDA_TOOLKIT_ROOT_DIR CMake parameter.

Side note: If you have multiple versions of CUDA installed on a Windows machine and are building with Visual Studio, CMake will use the build files for the highest version of CUDA it finds in the BuildCustomization folder. e.g. C:\Program Files (x86)\Microsoft Visual Studio\2017\Enterprise\Common7\IDE\VC\VCTargets\BuildCustomizations. If you want to build with an earlier version, you must temporarily remove the 'CUDA x.y.*' files for later versions from this directory.

The path to the 'cuda' folder in the CUDNN installation must be provided. The 'cuda' folder should contain 'bin', 'include' and 'lib' directories.

You can build with:

./build.sh --use_cuda --cudnn_home /usr --cuda_home /usr/local/cuda (Linux)
./build.bat --use_cuda --cudnn_home <cudnn home path> --cuda_home <cuda home path> (Windows)

MKL-DNN

To build ONNX Runtime with MKL-DNN support, build it with ./build.sh --use_mkldnn --use_mklml

OpenBLAS

Windows

Instructions how to build OpenBLAS for windows can be found here https://github.com/xianyi/OpenBLAS/wiki/How-to-use-OpenBLAS-in-Microsoft-Visual-Studio#build-openblas-for-universal-windows-platform.

Once you have the OpenBLAS binaries, build ONNX Runtime with ./build.bat --use_openblas

Linux

For Linux (e.g. Ubuntu 16.04), install libopenblas-dev package sudo apt-get install libopenblas-dev and build with ./build.sh --use_openblas

OpenMP

./build.sh --use_openmp (for Linux)
./build.bat --use_openmp (for Windows)

Build with Docker on Linux

Install Docker: https://docs.docker.com/install/

CPU

cd tools/ci_build/github/linux/docker
docker build -t onnxruntime_dev --build-arg OS_VERSION=16.04 -f Dockerfile.ubuntu .
docker run --rm -it onnxruntime_dev /bin/bash

GPU

If you need GPU support, please also install:

1. nvidia driver. Before doing this please add 'nomodeset rd.driver.blacklist=nouveau' to your linux kernel boot parameters.
2. nvidia-docker2: Install doc

To test if your nvidia-docker works:

docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi

Then build a docker image. We provided a sample for use:

cd tools/ci_build/github/linux/docker
docker build -t cuda_dev -f Dockerfile.ubuntu_gpu .

Then run it

./tools/ci_build/github/linux/run_dockerbuild.sh